Abstract 2735: An information theoretic approach to investigate AML development using time-series-omics
Authors: David E. Frankhouser, Sergio Branciamore, Denis O'Meally, Lisa Uechi, Guido Marcucci, Ya-Huei Kuo, Russell Rockne
Journal: Cancer Research (2022)

Abstract Introductory Statement: Using our previously published state-transition model applied to times-series micro-RNA (miRNA) expression samples, we construct a miRNA-based transcriptomic landscape of AML development to discover how miRNA dynamics contribute to leukemogenesis. Experimental Procedures: We used a genetic mouse model that mimics development of inv(16) AML through the induction of a leukemogenic fusion gene CBFB-MYH11 (CM). From monthly blood draws on both CM and control mice, we produced time-series messenger RNA (mRNA) and micro-RNA (miRNA) transcriptomic data. We have recently reported a state-transition model using the mRNA transcriptome data. We model the development of AML as a particle undergoing Brownian motion in a potential well with three critical points. Here, investigate whether the miRNA transcriptome can also be used to model AML development. Our current work is to extend the state-transition model using information theoretic approach to explore both the regulatory relationship between mRNA and miRNA and how a biological system transitions to a disease state. Summary of Results: Our state-transition model detects early perturbations of the system before it can be detected using cKit expression, the marker of AML blasts in this model. Since individual mice develop AML stochastically in time, we used the state-transition model to align the mice based on the phenotypic disease state rather than chronological time. Four distinct expression dynamics of miRNAs were identified with hierarchical clustering: persistent increase or decrease in expression; and two nonlinear patterns of expression. The nonlinear dynamic groups showed changes in expression at the critical transition point between health and AML. Additionally, the nonlinear expression dynamic patterns occurred at the boundary between the health and AML states was associated with cytokines and systemic inflammation signaling pathways, suggesting an anti-leukemic restorative miRNA program. Using regulatory dynamics in the state-transition model, we observed unique mRNA-miRNA regulatory interactions which may suggest miRNA gene targets at critical transition points in leukemia progression. Conclusions: Our information theoretic approach combined with our state-transition model is a promising approach to study AML development. This framework provides a method to integrate different -omic data without prior knowledge of the relationship between a gene and its regulatory features. Additionally, our approach can investigate the inter-individual heterogeneity of disease progression which has important implications for precision medicine. The state-transition analysis framework is a promising approach to explore the underlying principles that govern biological systems. Citation Format: David E. Frankhouser, Sergio Branciamore, Denis O'Meally, Lisa Uechi, Guido Marcucci, Ya-Huei Kuo, Russell Rockne. An information theoretic approach to investigate AML development using time-series-omics [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 2735.